A conventional method of seeking digital images in a database containing images is based on a system of indexing the images in the database.
The purpose of the indexing system is to associate, with each image in the database, an item of information characteristic of the content of the image referred to as the index of the image. All these information items form the index of the database.
A user can then interrogate the image database through a request containing an item of information characteristic of the type of image sought. The content of the request is then compared in accordance with a search strategy with the content of the index of the database.
Finally, the image in the database whose indexed information item has the greatest similarity to the content of the request is then extracted. A plurality of images extracted from the database can also be presented to the user, ordered according to their degree of similarity to the request.
In a traditional system of indexing digital images, the index of the database is composed of textual descriptors of the images stored.
The request of the user then consists of key words describing the characteristics of the content of the image to be sought.
This type of indexing by textual descriptors has the drawback of being imprecise, in particular because the same image may be described in different ways by different users.
In order to mitigate this type of drawback, the need has therefore been felt to develop techniques for representing and extracting the semantic content of a digital image.
Methods have appeared in which an image is characterised according to the distribution of the colours or textures making it up.
In other methods, an image is characterised by the shape or contour of an object making it up.
However, all these descriptors, referred to as “primitives”, of the image, reflect only physical characteristics of the image, and are therefore of a low semantic level.
In order to increase the semantic character of the indexing of the images, indexing systems which use a combination of low-level primitives are beginning to appear.
One of the most well-known is certainly the QBIC (“query-by-image-content”) system developed by IBM.
To obtain more details on this system, reference can be made to the article “The QBIC Project: Querying Images by Content Using Color, Texture, and Shape”, by Niblack, W et al., which appeared in IBM Computer Sciences Research Report, pp 1–20 (1st Feb. 1993). Reference can also be made to U.S. Pat. No. 5,579,471 of IBM entitled “Image query system and method”. 
The QBIC system makes it possible to find digital fixed (or video) images from a request by means of the example. In the particular case of fixed images, this request is defined either as a complete image or as an object with a rectangular or arbitrary shape, extracted from an image or defined by the user.
The content of the images in the database is characterised by its colour distribution (histogram), texture and shape.
Where the request is defined as a portion, also referred to as a region, of an image, the similarity measurement also takes account of the spatial position of this region.
However, the request relates at best only to a single portion of the example image.
Moreover, the user has no possibility of designating particular areas of the example image whose content he does not wish to find in the type of images which he is seeking.
In addition, the user cannot specify to the search system that he wishes for the search to be made only from the content of particular regions which he has defined in the example image, without taking into consideration the content of the remainder of the image.
These particular areas or regions of the example image are referred to, in the remainder of the description, as “regions of interest”.